Escalate Prognosis of Parkinson’s Disease Employing Wavelet Features and Artificial Intelligence from Vowel Phonation
Why this work is in the frame
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Bibliographic record
Abstract
Background: This work presents an artificial intelligence-based algorithm for detecting Parkinson’s disease (PD) from voice signals. The detection of PD at pre-symptomatic stages is imperative to slow disease progression. Speech signal processing-based PD detection can play a crucial role here, as it has been reported in the literature that PD affects the voice quality of patients at an early stage. Hence, speech samples can be used as biomarkers of PD, provided that suitable voice features and artificial intelligence algorithms are employed. Methods: Advanced signal-processing techniques are used to extract audio features from the sustained vowel ‘/a/’ sound. The extracted audio features include baseline features, intensities, formant frequencies, bandwidths, vocal fold parameters, and Mel-frequency cepstral coefficients (MFCCs) to form a feature vector. Then, this feature vector is further enriched by including wavelet-based features to form the second feature vector. For classification purposes, two popular machine learning models, namely, support vector machine (SVM) and k-nearest neighbors (kNNs), are trained to distinguish patients with PD. Results: The results demonstrate that the inclusion of wavelet-based voice features enhances the performance of both the SVM and kNN models for PD detection. However, kNN provides better accuracy, detection speed, training time, and misclassification cost than SVM. Conclusions: This work concludes that wavelet-based voice features are important for detecting neurodegenerative diseases like PD. These wavelet features can enhance the classification performance of machine learning models. This work also concludes that kNN is recommendable over SVM for the investigated voice features, despite the inclusion and exclusion of the wavelet features.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it